De-interlacing is a process for converting an interlaced scan into a non-interlaced scan. Such a function is necessary in digital TV systems where the input video may have many different video formats. If a digital TV monitor is non-interlaced and the input video is in an interlaced format, the video needs to be de-interlaced.
Detecting edge directions (orientations) between neighboring lines in an interlaced scan is important in de-interlacing. Along the edge direction, image pixels' luminance values remain constant or change gradually. However, across the edge direction, pixels' luminance values change sharply.
There are existing methods for image de-interlacing. Generally, these methods can be classified into three categories: spatial (or intra-field), temporal (or inter-field) and spatio-temporal. In a spatial method, only samples (i.e., pixels) in the same field are utilized to calculate a value for new pixels. In a temporal method, samples in the neighboring fields are used to calculate a value for the new pixels. In a spatio-temporal method, samples in both the current field and neighboring fields may be used to calculate a value for the new pixels. Recently, motion compensation is also being used for de-interlacing.
Among the various kinds of de-interlacing methods, the spatial method is the most fundamental one. When there is a large scene change in the video, temporal information may not be reliable for de-interlacing. In that case, the spatial method is usually used. In the motion compensation based de-interlacing, the spatial method is also used when a motion vector is not reliable. Therefore, good spatial de-interlacing is very important for the overall de-interlacing quality in a digital TV system.
The basic idea of a spatial de-interlacing method is to utilize the correlation among the neighboring samples around the position where a new pixel is to be interpolated. Generally, interpolation is performed by computing a weighted average of neighboring samples. However, one problem with this general type of spatial interpolation is the degradation of image edges, including serrate lines or blurred edges that may appear in the interpolated image.
One solution for the above problem is to perform interpolation along image edge direction. Such a method requires detection of image edge direction for each position to be interpolated. Then based on the edge direction, interpolation may be performed by computing a weighted average of neighboring samples along that direction.
Some methods have been proposed for interpolating image along edge direction. However, it is still a difficult and open issue how to effectively and accurately detecting the edge direction for each position to be interpolated. On one hand, the detection of edge directions must be accurate because a wrong edge direction may introduce obvious artifacts or errors into the interpolated image. On the other hand, the edge direction should be used effectively wherever it is available. Otherwise, if a good edge direction is not properly detected and used at a given position, interpolation at that position may cause degradation of the edge.